{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\">注</font>: 使用 tensorboard 可视化需要安装 tensorflow (TensorBoard依赖于tensorflow库，可以任意安装tensorflow的gpu/cpu版本)\n",
    "\n",
    "```shell\n",
    "pip install tensorflow-cpu\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T06:39:37.505092Z",
     "start_time": "2025-01-20T06:39:28.540647Z"
    }
   },
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=12, micro=3, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cpu\n",
      "cpu\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备\n",
    "\n",
    "```shell\n",
    "$ tree -L 2 archive \n",
    "archive\n",
    "├── monkey_labels.txt\n",
    "├── training\n",
    "│   ├── n0\n",
    "│   ├── n1\n",
    "│   ├── n2\n",
    "│   ├── n3\n",
    "│   ├── n4\n",
    "│   ├── n5\n",
    "│   ├── n6\n",
    "│   ├── n7\n",
    "│   ├── n8\n",
    "│   └── n9\n",
    "└── validation\n",
    "    ├── n0\n",
    "    ├── n1\n",
    "    ├── n2\n",
    "    ├── n3\n",
    "    ├── n4\n",
    "    ├── n5\n",
    "    ├── n6\n",
    "    ├── n7\n",
    "    ├── n8\n",
    "    └── n9\n",
    "\n",
    "22 directories, 1 file\n",
    "```"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 用于从 self.samples 中提取所有样本的类别索引（即每个元组的第二个元素）"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T06:39:38.752355Z",
     "start_time": "2025-01-20T06:39:37.505092Z"
    }
   },
   "source": [
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor, Resize, Compose, ConvertImageDtype, Normalize\n",
    "\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "DATA_DIR = Path(\"./archive/\")\n",
    "\n",
    "class MonkeyDataset(datasets.ImageFolder):\n",
    "    def __init__(self, mode, transform=None):\n",
    "        if mode == \"train\":\n",
    "            root = DATA_DIR / \"training\"\n",
    "        elif mode == \"val\":\n",
    "            root = DATA_DIR / \"validation\"\n",
    "        else:\n",
    "            raise ValueError(\"mode should be one of the following: train, val, but got {}\".format(mode))\n",
    "        super().__init__(root, transform)\n",
    "        self.imgs = self.samples\n",
    "        self.targets = [s[1] for s in self.samples]\n",
    "\n",
    "# resnet 要求的，见 https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html\n",
    "img_h, img_w = 224, 224\n",
    "transform = Compose([\n",
    "     Resize((img_h, img_w)),\n",
    "     ToTensor(),\n",
    "     Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "     ConvertImageDtype(torch.float),\n",
    "])\n",
    "\n",
    "\n",
    "train_ds = MonkeyDataset(\"train\", transform=transform)\n",
    "val_ds = MonkeyDataset(\"val\", transform=transform)\n",
    "\n",
    "print(\"load {} images from training dataset\".format(len(train_ds)))\n",
    "print(\"load {} images from validation dataset\".format(len(val_ds)))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load 1097 images from training dataset\n",
      "load 272 images from validation dataset\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T06:39:38.756387Z",
     "start_time": "2025-01-20T06:39:38.752355Z"
    }
   },
   "source": [
    "# 数据类别\n",
    "train_ds.classes, train_ds.class_to_idx"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['n0', 'n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8', 'n9'],\n",
       " {'n0': 0,\n",
       "  'n1': 1,\n",
       "  'n2': 2,\n",
       "  'n3': 3,\n",
       "  'n4': 4,\n",
       "  'n5': 5,\n",
       "  'n6': 6,\n",
       "  'n7': 7,\n",
       "  'n8': 8,\n",
       "  'n9': 9})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T06:39:38.775815Z",
     "start_time": "2025-01-20T06:39:38.756387Z"
    }
   },
   "source": [
    "# 图片路径 及 标签\n",
    "for fpath, label in train_ds.imgs:\n",
    "    print(fpath, label)\n",
    "    break\n",
    "\n",
    "for img, label in train_ds:\n",
    "    # c, h, w  label\n",
    "    print(img.shape, label)\n",
    "    break"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "archive\\training\\n0\\n0018.jpg 0\n",
      "torch.Size([3, 224, 224]) 0\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T06:39:38.778866Z",
     "start_time": "2025-01-20T06:39:38.775815Z"
    }
   },
   "source": [
    "# 遍历train_ds得到每张图片，计算每个通道的均值和方差\n",
    "def cal_mean_std(ds):\n",
    "    mean = 0.\n",
    "    std = 0.\n",
    "    for img, _ in ds:\n",
    "        mean += img.mean(dim=(1, 2))\n",
    "        std += img.std(dim=(1, 2))\n",
    "    mean /= len(ds)\n",
    "    std /= len(ds)\n",
    "    return mean, std\n",
    "\n",
    "# 经过 normalize 后 均值为0，方差为1\n",
    "# print(cal_mean_std(train_ds))"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T06:39:38.781799Z",
     "start_time": "2025-01-20T06:39:38.778866Z"
    }
   },
   "source": [
    "import torch.nn as nn\n",
    "from torch.utils.data.dataloader import DataLoader    \n",
    "\n",
    "batch_size = 16\n",
    "# 从数据集到dataloader\n",
    "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False)"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-23T02:19:28.364598500Z",
     "start_time": "2024-07-23T02:19:27.176829700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 3, 224, 224])\n",
      "torch.Size([16])\n"
     ]
    }
   ],
   "source": [
    "for imgs, labels in train_loader:\n",
    "    print(imgs.shape)\n",
    "    print(labels.shape)\n",
    "    break"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T07:28:55.526142Z",
     "start_time": "2025-01-20T07:28:55.358597Z"
    }
   },
   "source": [
    "from torchvision.models import resnet50\n",
    "\n",
    "\n",
    "# 加载resnet50模型以及预训练权重，冻结除最后一层外所有层的权重，去除最后一层并添加自定义分类层\n",
    "class ResNet50(nn.Module):\n",
    "    def __init__(self, num_classes=10, frozen=True):\n",
    "        super().__init__()\n",
    "        # 下载预训练权重\n",
    "        self.model = resnet50(weights=\"IMAGENET1K_V2\",) # 这里的weights参数可以选择\"IMAGENET1K_V2\"或None，None表示随机初始化\n",
    "        # 冻结前面的层\n",
    "        if frozen:\n",
    "            for param in self.model.parameters():\n",
    "                param.requires_grad = False # 冻结权重\n",
    "        # for param in self.model.layer4.parameters():\n",
    "        #     param.requires_grad = True  # 解冻 layer4\n",
    "        for name, param in self.model.named_parameters():\n",
    "            if name == \"layer4.2.conv3.weight\":\n",
    "                param.requires_grad = True  # 解冻该层\n",
    "        # 添加自定义分类层\n",
    "        # print(self.model)\n",
    "        print(self.model.fc.in_features) # 打印resnet50的最后一层的输入通道数\n",
    "        print(self.model.fc.out_features) # 打印resnet50的最后一层的输出通道数 1000\n",
    "        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)   # 自定义分类层,把resnet50的最后一层改为num_classes个输出\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "\n",
    "for idx, (key, value) in enumerate(ResNet50().named_parameters()):\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2048\n",
      "1000\n",
      "           model.conv1.weight           paramerters num: 9408\n",
      "            model.bn1.weight            paramerters num: 64\n",
      "             model.bn1.bias             paramerters num: 64\n",
      "      model.layer1.0.conv1.weight       paramerters num: 4096\n",
      "       model.layer1.0.bn1.weight        paramerters num: 64\n",
      "        model.layer1.0.bn1.bias         paramerters num: 64\n",
      "      model.layer1.0.conv2.weight       paramerters num: 36864\n",
      "       model.layer1.0.bn2.weight        paramerters num: 64\n",
      "        model.layer1.0.bn2.bias         paramerters num: 64\n",
      "      model.layer1.0.conv3.weight       paramerters num: 16384\n",
      "       model.layer1.0.bn3.weight        paramerters num: 256\n",
      "        model.layer1.0.bn3.bias         paramerters num: 256\n",
      "   model.layer1.0.downsample.0.weight   paramerters num: 16384\n",
      "   model.layer1.0.downsample.1.weight   paramerters num: 256\n",
      "    model.layer1.0.downsample.1.bias    paramerters num: 256\n",
      "      model.layer1.1.conv1.weight       paramerters num: 16384\n",
      "       model.layer1.1.bn1.weight        paramerters num: 64\n",
      "        model.layer1.1.bn1.bias         paramerters num: 64\n",
      "      model.layer1.1.conv2.weight       paramerters num: 36864\n",
      "       model.layer1.1.bn2.weight        paramerters num: 64\n",
      "        model.layer1.1.bn2.bias         paramerters num: 64\n",
      "      model.layer1.1.conv3.weight       paramerters num: 16384\n",
      "       model.layer1.1.bn3.weight        paramerters num: 256\n",
      "        model.layer1.1.bn3.bias         paramerters num: 256\n",
      "      model.layer1.2.conv1.weight       paramerters num: 16384\n",
      "       model.layer1.2.bn1.weight        paramerters num: 64\n",
      "        model.layer1.2.bn1.bias         paramerters num: 64\n",
      "      model.layer1.2.conv2.weight       paramerters num: 36864\n",
      "       model.layer1.2.bn2.weight        paramerters num: 64\n",
      "        model.layer1.2.bn2.bias         paramerters num: 64\n",
      "      model.layer1.2.conv3.weight       paramerters num: 16384\n",
      "       model.layer1.2.bn3.weight        paramerters num: 256\n",
      "        model.layer1.2.bn3.bias         paramerters num: 256\n",
      "      model.layer2.0.conv1.weight       paramerters num: 32768\n",
      "       model.layer2.0.bn1.weight        paramerters num: 128\n",
      "        model.layer2.0.bn1.bias         paramerters num: 128\n",
      "      model.layer2.0.conv2.weight       paramerters num: 147456\n",
      "       model.layer2.0.bn2.weight        paramerters num: 128\n",
      "        model.layer2.0.bn2.bias         paramerters num: 128\n",
      "      model.layer2.0.conv3.weight       paramerters num: 65536\n",
      "       model.layer2.0.bn3.weight        paramerters num: 512\n",
      "        model.layer2.0.bn3.bias         paramerters num: 512\n",
      "   model.layer2.0.downsample.0.weight   paramerters num: 131072\n",
      "   model.layer2.0.downsample.1.weight   paramerters num: 512\n",
      "    model.layer2.0.downsample.1.bias    paramerters num: 512\n",
      "      model.layer2.1.conv1.weight       paramerters num: 65536\n",
      "       model.layer2.1.bn1.weight        paramerters num: 128\n",
      "        model.layer2.1.bn1.bias         paramerters num: 128\n",
      "      model.layer2.1.conv2.weight       paramerters num: 147456\n",
      "       model.layer2.1.bn2.weight        paramerters num: 128\n",
      "        model.layer2.1.bn2.bias         paramerters num: 128\n",
      "      model.layer2.1.conv3.weight       paramerters num: 65536\n",
      "       model.layer2.1.bn3.weight        paramerters num: 512\n",
      "        model.layer2.1.bn3.bias         paramerters num: 512\n",
      "      model.layer2.2.conv1.weight       paramerters num: 65536\n",
      "       model.layer2.2.bn1.weight        paramerters num: 128\n",
      "        model.layer2.2.bn1.bias         paramerters num: 128\n",
      "      model.layer2.2.conv2.weight       paramerters num: 147456\n",
      "       model.layer2.2.bn2.weight        paramerters num: 128\n",
      "        model.layer2.2.bn2.bias         paramerters num: 128\n",
      "      model.layer2.2.conv3.weight       paramerters num: 65536\n",
      "       model.layer2.2.bn3.weight        paramerters num: 512\n",
      "        model.layer2.2.bn3.bias         paramerters num: 512\n",
      "      model.layer2.3.conv1.weight       paramerters num: 65536\n",
      "       model.layer2.3.bn1.weight        paramerters num: 128\n",
      "        model.layer2.3.bn1.bias         paramerters num: 128\n",
      "      model.layer2.3.conv2.weight       paramerters num: 147456\n",
      "       model.layer2.3.bn2.weight        paramerters num: 128\n",
      "        model.layer2.3.bn2.bias         paramerters num: 128\n",
      "      model.layer2.3.conv3.weight       paramerters num: 65536\n",
      "       model.layer2.3.bn3.weight        paramerters num: 512\n",
      "        model.layer2.3.bn3.bias         paramerters num: 512\n",
      "      model.layer3.0.conv1.weight       paramerters num: 131072\n",
      "       model.layer3.0.bn1.weight        paramerters num: 256\n",
      "        model.layer3.0.bn1.bias         paramerters num: 256\n",
      "      model.layer3.0.conv2.weight       paramerters num: 589824\n",
      "       model.layer3.0.bn2.weight        paramerters num: 256\n",
      "        model.layer3.0.bn2.bias         paramerters num: 256\n",
      "      model.layer3.0.conv3.weight       paramerters num: 262144\n",
      "       model.layer3.0.bn3.weight        paramerters num: 1024\n",
      "        model.layer3.0.bn3.bias         paramerters num: 1024\n",
      "   model.layer3.0.downsample.0.weight   paramerters num: 524288\n",
      "   model.layer3.0.downsample.1.weight   paramerters num: 1024\n",
      "    model.layer3.0.downsample.1.bias    paramerters num: 1024\n",
      "      model.layer3.1.conv1.weight       paramerters num: 262144\n",
      "       model.layer3.1.bn1.weight        paramerters num: 256\n",
      "        model.layer3.1.bn1.bias         paramerters num: 256\n",
      "      model.layer3.1.conv2.weight       paramerters num: 589824\n",
      "       model.layer3.1.bn2.weight        paramerters num: 256\n",
      "        model.layer3.1.bn2.bias         paramerters num: 256\n",
      "      model.layer3.1.conv3.weight       paramerters num: 262144\n",
      "       model.layer3.1.bn3.weight        paramerters num: 1024\n",
      "        model.layer3.1.bn3.bias         paramerters num: 1024\n",
      "      model.layer3.2.conv1.weight       paramerters num: 262144\n",
      "       model.layer3.2.bn1.weight        paramerters num: 256\n",
      "        model.layer3.2.bn1.bias         paramerters num: 256\n",
      "      model.layer3.2.conv2.weight       paramerters num: 589824\n",
      "       model.layer3.2.bn2.weight        paramerters num: 256\n",
      "        model.layer3.2.bn2.bias         paramerters num: 256\n",
      "      model.layer3.2.conv3.weight       paramerters num: 262144\n",
      "       model.layer3.2.bn3.weight        paramerters num: 1024\n",
      "        model.layer3.2.bn3.bias         paramerters num: 1024\n",
      "      model.layer3.3.conv1.weight       paramerters num: 262144\n",
      "       model.layer3.3.bn1.weight        paramerters num: 256\n",
      "        model.layer3.3.bn1.bias         paramerters num: 256\n",
      "      model.layer3.3.conv2.weight       paramerters num: 589824\n",
      "       model.layer3.3.bn2.weight        paramerters num: 256\n",
      "        model.layer3.3.bn2.bias         paramerters num: 256\n",
      "      model.layer3.3.conv3.weight       paramerters num: 262144\n",
      "       model.layer3.3.bn3.weight        paramerters num: 1024\n",
      "        model.layer3.3.bn3.bias         paramerters num: 1024\n",
      "      model.layer3.4.conv1.weight       paramerters num: 262144\n",
      "       model.layer3.4.bn1.weight        paramerters num: 256\n",
      "        model.layer3.4.bn1.bias         paramerters num: 256\n",
      "      model.layer3.4.conv2.weight       paramerters num: 589824\n",
      "       model.layer3.4.bn2.weight        paramerters num: 256\n",
      "        model.layer3.4.bn2.bias         paramerters num: 256\n",
      "      model.layer3.4.conv3.weight       paramerters num: 262144\n",
      "       model.layer3.4.bn3.weight        paramerters num: 1024\n",
      "        model.layer3.4.bn3.bias         paramerters num: 1024\n",
      "      model.layer3.5.conv1.weight       paramerters num: 262144\n",
      "       model.layer3.5.bn1.weight        paramerters num: 256\n",
      "        model.layer3.5.bn1.bias         paramerters num: 256\n",
      "      model.layer3.5.conv2.weight       paramerters num: 589824\n",
      "       model.layer3.5.bn2.weight        paramerters num: 256\n",
      "        model.layer3.5.bn2.bias         paramerters num: 256\n",
      "      model.layer3.5.conv3.weight       paramerters num: 262144\n",
      "       model.layer3.5.bn3.weight        paramerters num: 1024\n",
      "        model.layer3.5.bn3.bias         paramerters num: 1024\n",
      "      model.layer4.0.conv1.weight       paramerters num: 524288\n",
      "       model.layer4.0.bn1.weight        paramerters num: 512\n",
      "        model.layer4.0.bn1.bias         paramerters num: 512\n",
      "      model.layer4.0.conv2.weight       paramerters num: 2359296\n",
      "       model.layer4.0.bn2.weight        paramerters num: 512\n",
      "        model.layer4.0.bn2.bias         paramerters num: 512\n",
      "      model.layer4.0.conv3.weight       paramerters num: 1048576\n",
      "       model.layer4.0.bn3.weight        paramerters num: 2048\n",
      "        model.layer4.0.bn3.bias         paramerters num: 2048\n",
      "   model.layer4.0.downsample.0.weight   paramerters num: 2097152\n",
      "   model.layer4.0.downsample.1.weight   paramerters num: 2048\n",
      "    model.layer4.0.downsample.1.bias    paramerters num: 2048\n",
      "      model.layer4.1.conv1.weight       paramerters num: 1048576\n",
      "       model.layer4.1.bn1.weight        paramerters num: 512\n",
      "        model.layer4.1.bn1.bias         paramerters num: 512\n",
      "      model.layer4.1.conv2.weight       paramerters num: 2359296\n",
      "       model.layer4.1.bn2.weight        paramerters num: 512\n",
      "        model.layer4.1.bn2.bias         paramerters num: 512\n",
      "      model.layer4.1.conv3.weight       paramerters num: 1048576\n",
      "       model.layer4.1.bn3.weight        paramerters num: 2048\n",
      "        model.layer4.1.bn3.bias         paramerters num: 2048\n",
      "      model.layer4.2.conv1.weight       paramerters num: 1048576\n",
      "       model.layer4.2.bn1.weight        paramerters num: 512\n",
      "        model.layer4.2.bn1.bias         paramerters num: 512\n",
      "      model.layer4.2.conv2.weight       paramerters num: 2359296\n",
      "       model.layer4.2.bn2.weight        paramerters num: 512\n",
      "        model.layer4.2.bn2.bias         paramerters num: 512\n",
      "      model.layer4.2.conv3.weight       paramerters num: 1048576\n",
      "       model.layer4.2.bn3.weight        paramerters num: 2048\n",
      "        model.layer4.2.bn3.bias         paramerters num: 2048\n",
      "            model.fc.weight             paramerters num: 20480\n",
      "             model.fc.bias              paramerters num: 10\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "source": [
    "model = ResNet50(num_classes=10, frozen=True)\n",
    "def count_parameters(model): #计算模型总参数量\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "count_parameters(model)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-20T07:28:58.912249Z",
     "start_time": "2025-01-20T07:28:58.749737Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2048\n",
      "1000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1069066"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T07:08:10.086234Z",
     "start_time": "2025-01-20T07:08:10.083273Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20490"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "source": "model",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-20T06:48:29.601561Z",
     "start_time": "2025-01-20T06:48:29.597356Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet50(\n",
       "  (model): ResNet(\n",
       "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (layer1): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer2): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer3): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (4): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (5): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer4): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "    (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "source": [
    "total_params = sum(p.numel() for p in model.parameters() )\n",
    "print(f\"Total trainable parameters: {total_params}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-20T06:48:57.859769Z",
     "start_time": "2025-01-20T06:48:57.855987Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total trainable parameters: 23528522\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "source": [
    "m = nn.AdaptiveAvgPool2d(output_size=(1, 1))\n",
    "input = torch.randn(1, 2048, 9, 9)\n",
    "output = m(input)\n",
    "output.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-20T06:52:19.752248Z",
     "start_time": "2025-01-20T06:52:19.747379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 2048, 1, 1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "2359296"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "512*3*3*512"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-23T07:06:55.092794100Z",
     "start_time": "2024-07-23T07:06:55.088043800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "1048576"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "512*1*1*2048"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-23T03:45:55.270615100Z",
     "start_time": "2024-07-23T03:45:55.261439200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "32.0"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "512/16"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-23T02:56:07.349270600Z",
     "start_time": "2024-07-23T02:56:07.337722300Z"
    }
   }
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练\n",
    "\n",
    "pytorch的训练需要自行实现，包括\n",
    "1. 定义损失函数\n",
    "2. 定义优化器\n",
    "3. 定义训练步\n",
    "4. 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-23T02:59:12.386898800Z",
     "start_time": "2024-07-23T02:59:11.605525600Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    pred_list = []\n",
    "    label_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item())\n",
    "        \n",
    "        preds = logits.argmax(axis=-1)    # 验证集预测\n",
    "        pred_list.extend(preds.cpu().numpy().tolist())\n",
    "        label_list.extend(labels.cpu().numpy().tolist())\n",
    "        \n",
    "    acc = accuracy_score(label_list, pred_list)\n",
    "    return np.mean(loss_list), acc\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorBoard 可视化\n",
    "\n",
    "\n",
    "训练过程中可以使用如下命令启动tensorboard服务。\n",
    "\n",
    "```shell\n",
    "tensorboard \\\n",
    "    --logdir=runs \\     # log 存放路径\n",
    "    --host 0.0.0.0 \\    # ip\n",
    "    --port 8848         # 端口\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "\n",
    "class TensorBoardCallback:\n",
    "    def __init__(self, log_dir, flush_secs=10):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            log_dir (str): dir to write log.\n",
    "            flush_secs (int, optional): write to dsk each flush_secs seconds. Defaults to 10.\n",
    "        \"\"\"\n",
    "        self.writer = SummaryWriter(log_dir=log_dir, flush_secs=flush_secs)\n",
    "\n",
    "    def draw_model(self, model, input_shape):\n",
    "        self.writer.add_graph(model, input_to_model=torch.randn(input_shape))\n",
    "        \n",
    "    def add_loss_scalars(self, step, loss, val_loss):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/loss\", \n",
    "            tag_scalar_dict={\"loss\": loss, \"val_loss\": val_loss},\n",
    "            global_step=step,\n",
    "            )\n",
    "        \n",
    "    def add_acc_scalars(self, step, acc, val_acc):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/accuracy\",\n",
    "            tag_scalar_dict={\"accuracy\": acc, \"val_accuracy\": val_acc},\n",
    "            global_step=step,\n",
    "        )\n",
    "        \n",
    "    def add_lr_scalars(self, step, learning_rate):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/learning_rate\",\n",
    "            tag_scalar_dict={\"learning_rate\": learning_rate},\n",
    "            global_step=step,\n",
    "            \n",
    "        )\n",
    "    \n",
    "    def __call__(self, step, **kwargs):\n",
    "        # add loss\n",
    "        loss = kwargs.pop(\"loss\", None)\n",
    "        val_loss = kwargs.pop(\"val_loss\", None)\n",
    "        if loss is not None and val_loss is not None:\n",
    "            self.add_loss_scalars(step, loss, val_loss)\n",
    "        # add acc\n",
    "        acc = kwargs.pop(\"acc\", None)\n",
    "        val_acc = kwargs.pop(\"val_acc\", None)\n",
    "        if acc is not None and val_acc is not None:\n",
    "            self.add_acc_scalars(step, acc, val_acc)\n",
    "        # add lr\n",
    "        learning_rate = kwargs.pop(\"lr\", None)\n",
    "        if learning_rate is not None:\n",
    "            self.add_lr_scalars(step, learning_rate)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Best\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-23T02:59:18.296416Z",
     "start_time": "2024-07-23T02:59:18.287854800Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        \"\"\"\n",
    "        Save checkpoints each save_epoch epoch. \n",
    "        We save checkpoint by epoch in this implementation.\n",
    "        Usually, training scripts with pytorch evaluating model and save checkpoint by step.\n",
    "\n",
    "        Args:\n",
    "            save_dir (str): dir to save checkpoint\n",
    "            save_epoch (int, optional): the frequency to save checkpoint. Defaults to 1.\n",
    "            save_best_only (bool, optional): If True, only save the best model or save each model at every epoch.\n",
    "        \"\"\"\n",
    "        self.save_dir = save_dir\n",
    "        self.save_step = save_step\n",
    "        self.save_best_only = save_best_only\n",
    "        self.best_metrics = -1\n",
    "        \n",
    "        # mkdir\n",
    "        if not os.path.exists(self.save_dir):\n",
    "            os.mkdir(self.save_dir)\n",
    "        \n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return\n",
    "        \n",
    "        if self.save_best_only:\n",
    "            assert metric is not None\n",
    "            if metric >= self.best_metrics:\n",
    "                # save checkpoints\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"best.ckpt\"))\n",
    "                # update best metrics\n",
    "                self.best_metrics = metric\n",
    "        else:\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Early Stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-23T02:59:21.080731100Z",
     "start_time": "2024-07-23T02:59:21.073276Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-23T03:00:28.829465300Z",
     "start_time": "2024-07-23T02:59:52.601793600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2048\n",
      "1000\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1380 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c718adea33e34666b38dbd8db07083a9"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[24], line 107\u001B[0m\n\u001B[0;32m    104\u001B[0m early_stop_callback \u001B[38;5;241m=\u001B[39m EarlyStopCallback(patience\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m5\u001B[39m)\n\u001B[0;32m    106\u001B[0m model \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mto(device)\n\u001B[1;32m--> 107\u001B[0m record \u001B[38;5;241m=\u001B[39m \u001B[43mtraining\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    108\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[0;32m    109\u001B[0m \u001B[43m    \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[0;32m    110\u001B[0m \u001B[43m    \u001B[49m\u001B[43mval_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[0;32m    111\u001B[0m \u001B[43m    \u001B[49m\u001B[43mepoch\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[0;32m    112\u001B[0m \u001B[43m    \u001B[49m\u001B[43mloss_fct\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[0;32m    113\u001B[0m \u001B[43m    \u001B[49m\u001B[43moptimizer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[0;32m    114\u001B[0m \u001B[43m    \u001B[49m\u001B[43mtensorboard_callback\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m    115\u001B[0m \u001B[43m    \u001B[49m\u001B[43msave_ckpt_callback\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msave_ckpt_callback\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    116\u001B[0m \u001B[43m    \u001B[49m\u001B[43mearly_stop_callback\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mearly_stop_callback\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    117\u001B[0m \u001B[43m    \u001B[49m\u001B[43meval_step\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    118\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[1;32mIn[24], line 50\u001B[0m, in \u001B[0;36mtraining\u001B[1;34m(model, train_loader, val_loader, epoch, loss_fct, optimizer, tensorboard_callback, save_ckpt_callback, early_stop_callback, eval_step)\u001B[0m\n\u001B[0;32m     48\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m global_step \u001B[38;5;241m%\u001B[39m eval_step \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[0;32m     49\u001B[0m     model\u001B[38;5;241m.\u001B[39meval()\n\u001B[1;32m---> 50\u001B[0m     val_loss, val_acc \u001B[38;5;241m=\u001B[39m \u001B[43mevaluating\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mval_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mloss_fct\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     51\u001B[0m     record_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mval\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mappend({\n\u001B[0;32m     52\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mloss\u001B[39m\u001B[38;5;124m\"\u001B[39m: val_loss, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124macc\u001B[39m\u001B[38;5;124m\"\u001B[39m: val_acc, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstep\u001B[39m\u001B[38;5;124m\"\u001B[39m: global_step\n\u001B[0;32m     53\u001B[0m     })\n\u001B[0;32m     54\u001B[0m     model\u001B[38;5;241m.\u001B[39mtrain()\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\utils\\_contextlib.py:115\u001B[0m, in \u001B[0;36mcontext_decorator.<locals>.decorate_context\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    112\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m    113\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mdecorate_context\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m    114\u001B[0m     \u001B[38;5;28;01mwith\u001B[39;00m ctx_factory():\n\u001B[1;32m--> 115\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[1;32mIn[20], line 12\u001B[0m, in \u001B[0;36mevaluating\u001B[1;34m(model, dataloader, loss_fct)\u001B[0m\n\u001B[0;32m     10\u001B[0m labels \u001B[38;5;241m=\u001B[39m labels\u001B[38;5;241m.\u001B[39mto(device)\n\u001B[0;32m     11\u001B[0m \u001B[38;5;66;03m# 前向计算\u001B[39;00m\n\u001B[1;32m---> 12\u001B[0m logits \u001B[38;5;241m=\u001B[39m \u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdatas\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     13\u001B[0m loss \u001B[38;5;241m=\u001B[39m loss_fct(logits, labels)         \u001B[38;5;66;03m# 验证集损失\u001B[39;00m\n\u001B[0;32m     14\u001B[0m loss_list\u001B[38;5;241m.\u001B[39mappend(loss\u001B[38;5;241m.\u001B[39mitem())\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1530\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[0;32m   1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1532\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m   1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m   1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[0;32m   1539\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m   1540\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1541\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1544\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
      "Cell \u001B[1;32mIn[19], line 22\u001B[0m, in \u001B[0;36mResNet50.forward\u001B[1;34m(self, x)\u001B[0m\n\u001B[0;32m     21\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, x):\n\u001B[1;32m---> 22\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1530\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[0;32m   1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1532\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m   1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m   1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[0;32m   1539\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m   1540\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1541\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1544\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torchvision\\models\\resnet.py:285\u001B[0m, in \u001B[0;36mResNet.forward\u001B[1;34m(self, x)\u001B[0m\n\u001B[0;32m    284\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, x: Tensor) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Tensor:\n\u001B[1;32m--> 285\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_forward_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torchvision\\models\\resnet.py:273\u001B[0m, in \u001B[0;36mResNet._forward_impl\u001B[1;34m(self, x)\u001B[0m\n\u001B[0;32m    270\u001B[0m x \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrelu(x)\n\u001B[0;32m    271\u001B[0m x \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmaxpool(x)\n\u001B[1;32m--> 273\u001B[0m x \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlayer1\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    274\u001B[0m x \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlayer2(x)\n\u001B[0;32m    275\u001B[0m x \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlayer3(x)\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1530\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[0;32m   1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1532\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m   1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m   1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[0;32m   1539\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m   1540\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1541\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1544\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\container.py:217\u001B[0m, in \u001B[0;36mSequential.forward\u001B[1;34m(self, input)\u001B[0m\n\u001B[0;32m    215\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;28minput\u001B[39m):\n\u001B[0;32m    216\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m module \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m:\n\u001B[1;32m--> 217\u001B[0m         \u001B[38;5;28minput\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[43mmodule\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m    218\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28minput\u001B[39m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1530\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[0;32m   1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1532\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m   1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m   1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[0;32m   1539\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m   1540\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1541\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1544\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torchvision\\models\\resnet.py:161\u001B[0m, in \u001B[0;36mBottleneck.forward\u001B[1;34m(self, x)\u001B[0m\n\u001B[0;32m    158\u001B[0m     identity \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdownsample(x)\n\u001B[0;32m    160\u001B[0m out \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m identity\n\u001B[1;32m--> 161\u001B[0m out \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrelu\u001B[49m\u001B[43m(\u001B[49m\u001B[43mout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    163\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m out\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1530\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[0;32m   1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1532\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m   1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m   1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[0;32m   1539\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m   1540\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1541\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1544\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\modules\\activation.py:103\u001B[0m, in \u001B[0;36mReLU.forward\u001B[1;34m(self, input)\u001B[0m\n\u001B[0;32m    102\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;28minput\u001B[39m: Tensor) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Tensor:\n\u001B[1;32m--> 103\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mF\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrelu\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minplace\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43minplace\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\nn\\functional.py:1498\u001B[0m, in \u001B[0;36mrelu\u001B[1;34m(input, inplace)\u001B[0m\n\u001B[0;32m   1496\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m handle_torch_function(relu, (\u001B[38;5;28minput\u001B[39m,), \u001B[38;5;28minput\u001B[39m, inplace\u001B[38;5;241m=\u001B[39minplace)\n\u001B[0;32m   1497\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m inplace:\n\u001B[1;32m-> 1498\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrelu_\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1499\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1500\u001B[0m     result \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mrelu(\u001B[38;5;28minput\u001B[39m)\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits = model(datas)\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                preds = logits.argmax(axis=-1)\n",
    "            \n",
    "                acc = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())    \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                \n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"acc\": acc, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss, val_acc = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"acc\": val_acc, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "                    \n",
    "                    # 1. 使用 tensorboard 可视化\n",
    "                    if tensorboard_callback is not None:\n",
    "                        tensorboard_callback(\n",
    "                            global_step, \n",
    "                            loss=loss, val_loss=val_loss,\n",
    "                            acc=acc, val_acc=val_acc,\n",
    "                            lr=optimizer.param_groups[0][\"lr\"],\n",
    "                            )\n",
    "                \n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), metric=val_acc)\n",
    "\n",
    "                    # 3. 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(val_acc)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict\n",
    "        \n",
    "\n",
    "epoch = 20\n",
    "\n",
    "model = ResNet50(num_classes=10)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = nn.CrossEntropyLoss()\n",
    "# 2. 定义优化器 采用 sgd\n",
    "# Optimizers specified in the torch.optim package\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.0)\n",
    "\n",
    "# 1. tensorboard 可视化\n",
    "if not os.path.exists(\"runs\"):\n",
    "    os.mkdir(\"runs\")\n",
    "# tensorboard_callback = TensorBoardCallback(\"runs/monkeys-resnet50\")\n",
    "# tensorboard_callback.draw_model(model, [1, 3, img_h, img_w])\n",
    "# 2. save best\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(\"checkpoints/monkeys-resnet50\", save_step=len(train_loader), save_best_only=True)\n",
    "# 3. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=5)\n",
    "\n",
    "model = model.to(device)\n",
    "record = training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_loader)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "'model_architecture.png'"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 画图\n",
    "\n",
    "import torch\n",
    "from torchviz import make_dot\n",
    "\n",
    "# Assuming your model is already defined and named 'model'\n",
    "# Construct a dummy input\n",
    "dummy_input = torch.randn(1, 3, 224, 224)  # Replace with your input shape\n",
    "\n",
    "# Forward pass to generate the computation graph\n",
    "output = model(dummy_input)\n",
    "\n",
    "# Visualize the model architecture\n",
    "dot = make_dot(output, params=dict(model.named_parameters()))\n",
    "dot.render(\"model_architecture\", format=\"png\")  # Save the visualization as an image\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-23T03:02:10.300044700Z",
     "start_time": "2024-07-23T03:02:09.443403Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves(record_dict, sample_step=500):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    # plot\n",
    "    fig_num = len(train_df.columns)\n",
    "    fig, axs = plt.subplots(1, fig_num, figsize=(5 * fig_num, 5))\n",
    "    for idx, item in enumerate(train_df.columns):    \n",
    "        axs[idx].plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        axs[idx].plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        axs[idx].grid()\n",
    "        axs[idx].legend()\n",
    "        # axs[idx].set_xticks(range(0, train_df.index[-1], 5000))\n",
    "        # axs[idx].set_xticklabels(map(lambda x: f\"{int(x/1000)}k\", range(0, train_df.index[-1], 5000)))\n",
    "        axs[idx].set_xlabel(\"step\")\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curves(record, sample_step=10)  #横坐标是 steps"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.7935\n",
      "accuracy: 0.9816\n"
     ]
    }
   ],
   "source": [
    "# dataload for evaluating\n",
    "\n",
    "# load checkpoints\n",
    "model.load_state_dict(torch.load(\"checkpoints/monkeys-resnet50/best.ckpt\", map_location=\"cpu\"))\n",
    "\n",
    "model.eval()\n",
    "loss, acc = evaluating(model, val_loader, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\\naccuracy: {acc:.4f}\")"
   ]
  }
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